Fault detection system and method using approximate null space base fault signature classification

ABSTRACT

A system and method for fault detection is provided. The fault detection system provides the ability to detect symptoms of fault in turbine engines and other mechanical systems that have nonlinear relationships between two or more variables. The fault detection system uses a neural network to perform feature extraction from data for representation of faulty or normal conditions. The values of extracted features, referred to herein as scores, are then used to determine the likelihood of fault in the system. Specifically, the lower order scores, referred to herein as “approximate null space” scores can be classified into one or more clusters, where some clusters represent types of faults in the turbine engine. Classification based on the approximate null space scores provides the ability to classify faulty or nominal conditions that could not be reliably classified using higher order scores.

CROSS-REFERENCES TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.60/686,484, filed May 31, 2005.

FIELD OF THE INVENTION

This invention generally relates to diagnostic systems, and morespecifically relates to fault detection under both transient andsteady-state conditions.

BACKGROUND OF THE INVENTION

Modern aircraft are increasingly complex. The complexities of theseaircraft have led to an increasing need for automated fault detectionsystems. These fault detection systems are designed to monitor thevarious systems of the aircraft in an effect to detect potential faults.These systems are designed to detect these potential faults such thatthe potential faults can be addressed before the potential faults leadto serious system failure and possible in-flight shutdowns, take-offaborts, and delays or cancellations.

Engines are, of course, a particularly critical part of the aircraft. Assuch, fault detection for aircraft engines are an important part of anaircraft's fault detection system. Some traditional engine faultdetection has been limited to methods of fault detection that are basedon linear relationships between variables in the system. While thesemethods have been effective in detecting some faults, they are lesseffective in detecting faults in systems where there are significantnonlinearities in the system. Many complex systems, such as turbineengines, have substantially nonlinear relationships between variables inthe system. In these types of system, the nonlinear relationship betweenvariables reduces the effectiveness of these linear techniques for faultdetection.

Thus, what is needed is an improved system and method for faultdetection that is able to detect and classify fault in systems withnonlinear relationships among variables or observed measurements.

BRIEF SUMMARY OF THE INVENTION

The present invention provides an improved fault detection system andmethod. The fault detection system provides the ability to detectsymptoms of fault in turbine engines and other mechanical systems thathave nonlinear relationships between two or more variables. The faultdetection system uses a neural network to perform feature extractionfrom data for representation of faulty or normal conditions. The valuesof extracted features, referred to herein as scores, are then used todetermine the likelihood of fault in the system. The features arearranged in descending order of their ability to explain variancepresent in the data and the scores which explain lesser magnitudes ofvariance present in the data will henceforth be referred to as “lowerorder scores”. Specifically, the lower order scores, referred to hereinas “approximate null space” scores can be classified into one or moreclusters, where some clusters represent types of faults in the turbineengine. Classification based on the approximate null space scoresprovides the ability to classify faulty or nominal conditions that couldnot be reliably classified using higher order scores. Thus, the systemis able to reliably detect and classify faults in situations where othertechniques cannot.

In one embodiment the fault detection system includes a chain ofencoding neural networks and decoding neural networks. The chain ofencoding neural networks and decoding neural networks receives sensordata from the turbine engine and performs a principal component-typeanalysis to create a reduced feature space data representation of thesensor data. Specifically, the first encoding neural network receivesthe sensor data and generates a score, where the score is analogous to afirst principal component. The first decoding neural network receivesthe score from the first encoding neural network and outputsreconstructed estimate of the sensor data. The reconstructed estimate ofthe sensor data is subtracted from the sensor data to create a sensordata residual, which is passed to the second encoding neural network.The second encoding neural network generates a second score, where thesecond score is analogous to a second principal component. The seconddecoding neural network receives the second score from the seconddecoding neural network and outputs reconstructed estimate of the sensordata residual. The reconstructed estimate of the sensor data residual issubtracted from the original sensor data residual to create a secondsensor data residual, which is passed to the next encoding neuralnetwork. The chain of encoding neural networks and decoding neuralnetworks continues, creating a plurality N of scores, a sensor dataestimate, and N−1 residual estimates.

So implemented, the plurality of generated scores can be used for faultdetection and classification. Specifically, the lower order scores canbe used to classify the sensor data into one or more clusters, wheresome clusters represent types of faults in the turbine engine. In oneembodiment, classification is accomplished by passing the approximatenull space scores to one or more discriminant functions. Thediscriminant functions each represent a type of behavior in the system,such as a properly performing system, or a specific type of fault in thesystem. When the null space scores are inputted in the discriminantfunction, the output of the discriminant function will indicate whichside of a decision boundary the scores reside on, and thus whether thescores are in a good engine cluster, or in a particular bad enginecluster. Thus, the output of the discriminant functions is used toaccurately classify the performance of the turbine engine.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescription of a preferred embodiment of the invention, as illustratedin the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The preferred exemplary embodiment of the present invention willhereinafter be described in conjunction with the appended drawings,where like designations denote like elements, and:

FIG. 1 is a schematic view of an approximate null space neural networkfault detection system in accordance with an embodiment of theinvention;

FIG. 2 is a schematic view of a discriminant based classifier inaccordance with one exemplary embodiment;

FIG. 3 is a schematic view of a encoding and decoding network inaccordance with one exemplary embodiment;

FIG. 4 is a schematic view of a computer system that includes a neuralnetwork fault detection program; and

FIG. 5 are graphical views of exemplary score clusters.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a fault detection system and method. Thefault detection system provides the ability to detect symptoms of faultin turbine engines and other mechanical systems that have nonlinearrelationships among variables describing the system. The fault detectionsystem uses a neural network to perform a data representation andfeature extraction where the extracted features are analogous to eigenvectors derived from eigen decomposition of the covariance matrix of thedata. The extracted features, referred to herein as scores, are thenused to determine the likelihood of fault in the system. Specifically,the lower order scores, referred to herein as “approximate null space”scores can form one or more clusters, where some clusters representtypes of faults in the turbine engine. Classification based on theapproximate null space scores provides the ability to classify faultsignatures that could not be reliably classified using higher orderscores. Thus, the system is able to reliably detect and classify faultsin situations where other techniques cannot.

Turning now to FIG. 1, a neural network null space fault detectionsystem 100 is illustrated. The neural network null space fault detectionsystem 100 includes a chain of encoding neural networks 102 and decodingneural networks 104. The chain of encoding neural networks 102 anddecoding neural networks 104 receives sensor data from the system beingmonitored, such as a turbine engine, and performs a principalcomponent-type analysis to create a reduced feature space datarepresentation of the sensor data. Specifically, the sensor data ispassed to the first encoding neural network 102 (labeled encoding neuralnetwork 1). The first encoding neural network 102 receives the sensordata and generates a score (score 1), where the score is analogous to afirst principal component. The first decoding neural network 104(labeled decoding neural network 1) receives the score from the firstencoding neural network 102 and outputs reconstructed estimate of thesensor data. The reconstructed estimate of the sensor data is subtractedfrom the sensor data to create a sensor data residual, which is passedto the second encoding neural network 102. The second encoding neuralnetwork 102 generates a second score (score 2), where the second scoreis analogous to a second principal component. The second decoding neuralnetwork 104 receives the second score from the second encoding neuralnetwork and outputs a reconstructed estimate of the sensor data residual(residual estimate 1). The reconstructed estimate of the sensor dataresidual is subtracted from the original sensor data residual to createa second sensor data residual, which is passed to the next encodingneural network 102. The chain of encoding neural networks 102 anddecoding neural networks 104 continues, creating a plurality N ofscores, a sensor data estimate, and N−1 residual estimates.

It should be noted that the neural network fault detection system 100uses a feed forward neural network. These feed forward neural networkscan be trained using supervised techniques that use a target value, anactual output value, and some function of the difference between the twoas error.

So implemented, the plurality of generated scores can be used for faultdetection and classification. Specifically, the lower order scores,referred to herein as “approximate null space” scores can be used toclassify the sensor data into one or more clusters, where some clustersrepresent types of faults in the turbine engine. Classification based onthe approximate null space scores provides the ability to classifyfaults that could not be reliably classified using higher order scores.

Specifically, when the total variance is high and the variance inindividual clusters is low, the major components (i.e., higher orderscores) may be enough to provide separation of different classes.However, in other cases the major components by themselves will beinsufficient to reliably classify the behavior. In those cases, thelower order components, called the “approximate null space scores” areparticularly useful for classifying faults because they can be used toeffect separation of points among several clusters when it would not bereliably possible relying only on major components.

Scores corresponding to the nonlinear components which include a largerproportion of variance present in the data help in segregating differentclasses. For example, assuming that total variance of all the data(pooling the different classes together) is high and the variance of theindividual clusters are low, major components are typically sufficientfor separation of whole cluster of points. However, in the case whenthis is not the situation the minor components can be used to effectseparation of most of the points among several clusters.

It should be noted that the distinction between major components andminor components, i.e., the line between higher order scores andapproximate null space scores would depend upon the details of theapplication. Additionally, several different methods can be used todetermine what approximate null space scores can be used forclassification. For example, by determining the “intrinsicdimensionality” of the nonlinear data. In this case the scores beyondthe intrinsic dimension of all components are minor, approximate nullspace scores.

In one embodiment, the lower order scores are classified using a“discriminant” method. In a discriminant based method the bordersbetween clusters are determined and new scores are classified bydetermining what side of the borders the scores reside on. Theapproximate null space scores include discrimination features which areparticularly useful in building discriminant functions. Thus, theapproximate null space scores can be used to help in segregating notonly the cluster centroids in the score space but also most of thepoints belonging to two or more different classes.

The use of a discriminant based method offers some advantages over“representation” based techniques. Specifically, in representation basedtechniques classification is done by capturing the best representationfor each cluster in the space of scores. While representation basedtechniques may work for some classification problems they cannot be usedin all applications.

As stated above, the approximate null space scores are particularlyuseful for classifying faults because they can be used to effectseparation of points among several clusters when it would not bereliably possible relying only on major components. This facilitates theuse of a discriminant based classification technique. In one embodiment,classification is accomplished by passing the approximate null spacescores to one or more discriminant functions. Turning now to FIG. 2, anexemplary discriminant feature based classifier 150 in accordance withan embodiment of the invention is illustrated. The discriminant featurebased classifier 150 uses one or more discriminant functions 152 toclassify the approximate null space scores 154 generated by the neuralnetwork fault detection system. The classification of the scores is usedto generate a likelihood of fault 156, which can then be passed to adiagnostic system for further evaluation.

The discriminant functions 152 each represent a type of behavior in thesystem, such as a properly performing system, or a specific type offault in the system. Specifically, the discriminant functions capturethe characteristics of the separating boundaries between types of scoreclusters. When the approximate null space scores 154 are inputted in thediscriminant function 152, the output of the discriminant function 152will indicate which side of a decision boundary the scores reside on,and thus whether the scores are in a good engine cluster, or a badengine cluster.

A variety of different techniques can be used to develop thediscriminant functions used for classification. In one method, thediscriminant functions are developed using artificial neural networks(ANN). Using an ANN technique, discriminant functions can be developedfor each different cluster of scores. Specifically, ANN is used tocapture the characteristics of the decision, or separating boundariesbetween and among the various classes of scores. In doing so, the ANNconsiders the characteristics of the clusters that share commonboundaries. Thus, the features that represent the boundaries betweenclusters are discovered, and can be any line, plane, or curved surfacein the feature space.

Thus, when developed, new scores for the neural network null space faultdetection system can be inputted into the discriminant functions. Theresulting output (e.g., positive or negative) indicates which side ofthe corresponding decision boundary that score is in. Thus, by inputtingthe score into an appropriate set of discriminant functions, the clusterof the score can be determined. A typical discriminant functioncorresponding to a class usually has features for that class alone asparameters. In contrast, a discriminant function can utilized which usesfeatures from all the classes as parameters. This affords greaterdiscrimination power to the classifier developed. Turning briefly toFIG. 5, in one illustrated embodiment new data points are classifiedbased on a weighted distance measure based on their relativediscrimination abilities. Specifically, FIG. 5 illustrates graphicalrepresentations for nine combinations of approximate null space scoresfrom faulty turbine engines (Faulty PC1, Faulty PC2, Faulty PC3), withthe clusters represented by crosses, and approximate null space scoresfor nominal turbine engines (Nominal PC1, Nominal PC2, Nominal PC3),with the clusters represented by dots. The separation between clusterscan be used to classify the scores in those clusters. As can be seenfrom these graphs, some combination of features (e.g., Faulty PC3 andNominal PC1; Faulty PC2 and Nominal PC2; and Faulty PC3 and Nominal PC1)have very poor discrimination power whereas other combinations offeatures (e.g., Faulty PC2 and Nominal PC1; Faulty PC3 and Nominal PC2;and Faulty PC2 and Nominal PC3) have very high discrimination power.Thus, while constructing discriminant functions for each of the twoclasses, namely, Nominal and Faulty, the latter set of mixed featureswill have higher weightage as opposed to the former set of mixedfeatures, which may even have zero weightage associated with them

New class detection can be provided for cases where projection lengthsonto two or more different classification directions are notsignificantly different. Specifically, if the magnitude of distancesfrom two or more classes is less than a threshold, then it can bedetermined that the difference between the distances is not“statistically significant”. In those cases, the new data is assumed tobelong to a “new” class.

To create a null space fault detection system, a set of training datawould typically be used. The training data would be split into nominaltraining, nominal testing, faulty training and faulty testing data. Thetraining data would then be normalized and used to train the neuralnetwork fault detector. Lower end components, such as the three trailingcomponents, can then be taken and used as null space directions. Theclassification error for different combinations of null space componentscan be found, and based on these classification errors thediscriminatory weights for the neural network. Normalized test data canthen be used to find the null space components for the test data. Theprincipal components can then be weighted, and their weights used toassign a class label based on the least distance from a particular classcluster.

Turning now to FIG. 3, an exemplary embodiment of a neural network faultdetector 200 is illustrated schematically. The neural network faultdetector 200 includes an encoding neural network 202 and a decodingneural network 204. The encoding neural network 202 and a decodingneural network 204 are examples of type of neural networks that can beused in the chain of neural networks illustrated in FIG. 1.

In general, neural networks are data processing systems that are notexplicitly programmed. Instead, neural networks are trained throughexposure to real-time or historical data. Neural networks arecharacterized by powerful pattern matching and predictive capabilitiesin which input variables interact heavily. Through training, neuralnetworks learn the underlying relationships among the input and outputvariables, and form generalizations that are capable of representing anynonlinear function. As such, neural networks are a powerful technologyfor nonlinear, complex classification problems.

The encoding neural network 202 receives data inputs 206. For the firstencoding neural network this would comprise sensor data from the systembeing monitored, such as from a turbine engine. In some embodiments thesensor data comprises raw sensor data. In other embodiments, the sensordata is preprocessed using a suitable technique. For example, the sensordata can be preprocessed by passing through a semi-empirical polynomialmodel of the system to correct and normalize them for varying operatingconditions and to account for system specific idiosyncrasies. For laterencoding neural networks, the data inputs would comprise residualscreated by subtracted estimates from previous values.

The encoding neural network 202 performs a principal component-typeanalysis to create a reduced feature space data representation of thesensor data 206. The reduced feature space data representation is in theform of a score 208, where the score 208 is analogous to principalcomponents. Thus, the score generated by the first encoding neuralnetwork in the chain is analogous to the first principal component, thescore generated by the second encoding neural network is analogous tothe second principal component, and so on.

The score 208 is passed to a corresponding decoding neural network 204.The decoding neural network 204 receives the score, and outputsreconstructed estimate. The output of the first decoding neural networkwill comprise a reconstructed estimate of the sensor data, and outputsof later decoding neural networks will comprise reconstructed estimatesof residuals.

In the illustrated embodiment, both the encoding neural network 202 andthe decoding neural network 204 comprise multi-layered feed-forwardneural networks. Specifically, the encoding neural network 202 comprisesan input layer 212, a mapping layer 214 and an output layer 216. Thedecoding neural network 204 likewise comprises an input layer 218, ademapping layer 218 and an output layer 222. Each of these layersincludes a plurality of nodes, with each node having a correspondingactivation function. Typically, the number of nodes in each layer woulddepend upon a variety of factors. For example, the number of nodes inthe input layer 212 of the encoding neural network 202 would typicallybe equal to the number of sensors providing data. For example, in aturbine engine system that provides ten different sensor datameasurements during each event, the input layer of the encoding neuralnetwork 202 would include ten nodes.

The one node in the output layer 216 of the encoding neural network 202corresponds to scores generated by the neural network. Taken together,the scores generated by each of the encoding neural networks in thechain are a reduced feature space data representation of the sensordata, and are analogous to principal components.

Both the mapping layer 214 and demapping layer 220 would typically havea larger number of nodes than the input layer. This is to ensure goodgeneralization and prevents the network from forming a look-up table.

Each node in the encoding and decoding neural network includes anactivation function. Specifically, each node takes weighted combinationsof the node inputs and applies it to an activation function to producesome output, which is then passed to other nodes in the network. Tofacilitate data representation of nonlinear relationships in the sensordata, the nodes of the mapping layer 214 and demapping layer 220 wouldtypically have nonlinear activation functions. Nonlinear activationfunctions produce a nonlinear output. The use of nonlinear activationfunctions facilitates the modeling of nonlinear data distributions. Asone example, the nonlinear activation function can comprise a sigmoidalactivation function. Specifically, the nonlinear activation functionσ(γ) can be defined as: $\begin{matrix}{{{\sigma(y)} = {\frac{1}{1 + {\exp\left( {{- y} + \theta} \right)}}\quad{where}}},{y = {\sum\limits_{i = 1}^{n}{x_{i}w_{i}\quad{and}\quad\theta\quad{is}\quad{{bias}.}}}}} & {{Equation}\quad 1}\end{matrix}$

In most embodiments, the output layer 216 and output layer 222 do notrequire nonlinear activation functions. In these layers, a more typicallinear activation function can be used. For example, a linear activationfunction σ(γ) can be defined as: $\begin{matrix}{{{\sigma(y)} = {y\quad{where}}},{y = {\sum\limits_{i = 1}^{n}{x_{i}w_{i}\quad{and}\quad\theta\quad{is}\quad{{bias}.}}}}} & {{Equation}\quad 2}\end{matrix}$

Again, it should be noted that the number of layers, and the number ofnodes in each layer illustrated in the neural network fault detector 200is merely exemplary, and that in other embodiments the number of nodesand number of layers could differ significantly from the illustratedexample.

Each node in the neural network has an associated weight or weightvector. Training the neural network determines the weights associatedwith each node in the network. The neural network is trained for faultdetection using sets of historical sensor data. When so trained, theneural network is used for fault detection by inputting new sensor datainto neural network and comparing new sensor data to the reconstructedestimates. Additionally, the neural network can be used for faultdetection by classifying the scores output from the encoding network. Inthis embodiment, the scores are classified by comparing the scores fromnew sensor data with scores generated from historical sensor data duringtraining.

When the encoding and decoding networks are so trained and tested, theneural network can effectively detect faults in systems with nonlinearrelationships between data. Specifically, when so trained withhistorical data that includes nonlinear relationships, the resultingnetwork will be able to extract features for fault detectioncapitalizing on the nonlinear relationships among input sensor data.These features are extracted in the form of scores, which can then beused to determine if there is a fault in the mechanical system.

The neural network null space fault detection system and method can beimplemented in wide variety of platforms. Turning now to FIG. 4, anexemplary computer system 50 is illustrated. Computer system 50illustrates the general features of a computer system that can be usedto implement the invention. Of course, these features are merelyexemplary, and it should be understood that the invention can beimplemented using different types of hardware that can include more ordifferent features. It should be noted that the computer system can beimplemented in many different environments, such as onboard an aircraftto provide onboard diagnostics, or on the ground to provide remotediagnostics. The exemplary computer system 50 includes a processor 10,an interface 130, a storage device 190, a bus 170 and a memory 180. Inaccordance with the preferred embodiments of the invention, the memorysystem 50 includes a neural network null space fault detection program.

The processor 110 performs the computation and control functions of thesystem 50. The processor 110 may comprise any type of processor,including single integrated circuits such as a microprocessor, or maycomprise any suitable number of integrated circuit devices and/orcircuit boards working in cooperation to accomplish the functions of aprocessing unit. In addition, processor 10 may comprise multipleprocessors implemented on separate systems. In addition, the processor10 may be part of an overall vehicle control, navigation, avionics,communication or diagnostic system. During operation, the processor 10executes the programs contained within memory 180 and as such, controlsthe general operation of the computer system 50.

Memory 180 can be any type of suitable memory. This would include thevarious types of dynamic random access memory (DRAM) such as SDRAM, thevarious types of static RAM (SRAM), and the various types ofnon-volatile memory (PROM, EPROM, and flash). It should be understoodthat memory 180 may be a single type of memory component, or it may becomposed of many different types of memory components. In addition, thememory 180 and the processor 110 may be distributed across severaldifferent computers that collectively comprise system 50. For example, aportion of memory 180 may reside on the vehicle system computer, andanother portion may reside on a ground based diagnostic computer.

The bus 170 serves to transmit programs, data, status and otherinformation or signals between the various components of system 100. Thebus 170 can be any suitable physical or logical means of connectingcomputer systems and components. This includes, but is not limited to,direct hard-wired connections, fiber optics, infrared and wireless bustechnologies.

The interface 130 allows communication to the system 50, and can beimplemented using any suitable method and apparatus. It can include anetwork interfaces to communicate to other systems, terminal interfacesto communicate with technicians, and storage interfaces to connect tostorage apparatuses such as storage device 190. Storage device 190 canbe any suitable type of storage apparatus, including direct accessstorage devices such as hard disk drives, flash systems, floppy diskdrives and optical disk drives. As shown in FIG. 3, storage device 190can comprise a disc drive device that uses discs 195 to store data.

In accordance with the preferred embodiments of the invention, thecomputer system 50 includes the neural network null space faultdetection program. Specifically during operation, the neural networknull space fault detection program is stored in memory 180 and executedby processor 110. When being executed by the processor 110, the neuralnetwork fault detection system monitors operation parameters to identifypotential faults.

As one example implementation, the neural network fault detection systemcan operate on data that is acquired from the system (e.g., turbineengine) and periodically uploaded to an internet website. The neuralnetwork analysis is performed by the web site and the results arereturned back to the technician or other user. Thus, the system can beimplemented as part of a web-based diagnostic and prognostic system.

It should be understood that while the present invention is describedhere in the context of a fully functioning computer system, thoseskilled in the art will recognize that the mechanisms of the presentinvention are capable of being distributed as a program product in avariety of forms, and that the present invention applies equallyregardless of the particular type of signal bearing media used to carryout the distribution. Examples of computer-readable signal bearing mediainclude: recordable media such as floppy disks, hard drives, memorycards and optical disks (e.g., disk 195), and transmission media such asdigital and analog communication links.

Thus, various embodiments of the present invention thus provide a faultdetection system and method detection system provides the ability todetect symptoms of fault in turbine engines and other mechanical systemsthat have nonlinear relationships. Specifically, the fault detectionsystem includes a chain of encoding neural networks and decoding neuralnetworks. The chain of encoding neural networks and decoding neuralnetworks receives sensor data from the turbine engine and performs aprincipal component-type analysis to create a reduced feature space datarepresentation of the sensor data. Specifically, the first encodingneural network receives the sensor data and generates a score, where thescore is analogous to a first principal component. So implemented, theplurality of generated scores can be used for fault detection andclassification. The lower order scores, referred to herein as“approximate null space” scores can be classified into one or moreclusters, where some clusters represent types of faults in the turbineengine. Classification based on the approximate null space scoresprovides the ability to classify scores that could not be reliablyclassified using higher order scores. Thus, the system is able toreliably detect and classify faults in situations where other techniquescannot.

The embodiments and examples set forth herein were presented in order tobest explain the present invention and its particular application and tothereby enable those skilled in the art to make and use the invention.However, those skilled in the art will recognize that the foregoingdescription and examples have been presented for the purposes ofillustration and example only. The description as set forth is notintended to be exhaustive or to limit the invention to the precise formdisclosed. Many modifications and variations are possible in light ofthe above teaching without departing from the spirit of the forthcomingclaims.

1. A fault detection system for detecting faults in a turbine engine,the fault detection system comprising: a neural network system, theneural network system adapted to receive sensor data from the turbineengine and generate a plurality of approximate null space scores thatrepresent discrimination features in the sensor data; and a discriminantbased classifier, the discriminant based classifier adapted to receivethe approximate null space scores and classify the approximate nullspace scores to determine a likelihood of fault in the turbine engine.2. The system of claim 1 wherein the neural network system comprises achain of neural networks, each of the chain of neural networks adaptedto generate a score from the sensor data, and wherein a subset of thechain of neural networks are adapted to generate the plurality ofapproximate null space scores.
 3. The system of claim 1 wherein theneural network system comprises a chain of neural networks, and whereineach of the chain of neural networks includes an encoding neural networkand a corresponding decoding neural network, wherein each encodingneural network creates a score from the sensor data, each scorecomprising a reduced feature space representation of the sensor data;and wherein each decoding neural network receives the score from thecorresponding encoding neural network and creates an estimate of thesensor data, wherein the estimate of the sensor data is passed to thenext encoding network in the chain of neural networks.
 4. The system ofclaim 1 wherein the discriminant based classifier comprises a pluralityof discriminant functions, each discriminant function capturingcharacteristics of a boundary between score clusters.
 5. The system ofclaim 4 wherein the plurality of discriminant functions comprisesdiscriminant functions developed using an artificial neural network. 6.The system of claim 4 wherein the discriminant based classifierdetermines which side of the boundary between score clusters theapproximate null space scores are on to classify the approximate nullspace scores.
 7. The system of claim 4 wherein the discriminant basedclassifier determines a new cluster exists when a magnitude of adistance from the projected point in the approximate null space to twoor more classes are not statistically significantly different.
 8. Amethod of detecting fault in a turbine engine, the method comprising thesteps of: receiving sensor data from the turbine engine; generating aplurality of approximate null space scores from the sensor data, theplurality of approximate null space scores representing discriminationfeatures in the sensor data; and classifying the null space scores basedon discrimination to determine a likelihood of fault in the turbineengine.
 9. The method of claim 8 wherein the step of generating aplurality of approximate null space scores comprises generating theplurality of approximate null space scores using a chain of neuralnetworks.
 10. The method of claim 9 wherein each of the chain of neuralnetworks comprises step of generating a plurality of approximate nullspace scores comprises an encoding neural network and a correspondingdecoding neural network.
 11. The method of claim 8 wherein the step ofclassifying the null space scores based on discrimination to determine alikelihood of fault in the turbine engine comprises using a plurality ofdiscriminant functions, each of plurality of discriminant functionscapturing characteristics of a boundary between score clusters.
 12. Themethod of claim 11 wherein the plurality of discriminant functionscomprises discriminant functions developed using an artificial neuralnetwork.
 13. The method of claim 8 wherein the step of classifying thenull space scores based on discrimination to determine a likelihood offault in the turbine engine comprises determining which side of aboundary between score clusters the approximate null space scores areon.
 14. A program product comprising: a) a fault detection program, thefault detection program including: a neural network system, the neuralnetwork system adapted to receive sensor data from a turbine engine andgenerate a plurality of approximate null space scores that representdiscrimination features in the sensor data; and a discriminant basedclassifier, the discriminant based classifier adapted to receive theapproximate null space scores and classify the approximate null spacescores to determine a likelihood of fault in the turbine engine; and b)computer-readable signal bearing media bearing said program.
 15. Theprogram product of claim 14 wherein the neural network system comprisesa chain of neural networks, each of the chain of neural networks adaptedto generate a score from the sensor data, and wherein a subset of thechain of neural networks are adapted generate the plurality ofapproximate null space scores.
 16. The program product of claim 14wherein the neural network system comprises a chain of neural networks,and wherein each of the chain of neural networks includes an encodingneural network and a corresponding decoding neural network, wherein eachencoding neural network creates a score from the sensor data, each scorecomprising a reduced feature space representation of the sensor data;and wherein each decoding neural network receives the score from thecorresponding encoding neural network and creates an estimate of thesensor data, wherein the estimate of the sensor data is passed to thenext encoding network in the chain of neural networks.
 17. The programproduct of claim 14 wherein the discriminant based classifier comprisesa plurality of discriminant functions, each discriminant functioncapturing characteristics of a boundary between score clusters.
 18. Theprogram product of claim 17 wherein the plurality of discriminantfunctions comprises discriminant functions developed using an artificialneural network.
 19. The program product of claim 17 wherein thediscriminant based classifier determines which side of the boundarybetween score clusters the approximate null space scores are on toclassify the approximate null space scores.
 20. The program product ofclaim 17 wherein the discriminant based classifier determines a newcluster exists when a magnitude of a distance from the projected pointin the approximate null space to two or more classes are notstatistically significantly different.